Conventional data storage techniques can employ convolution and deconvolution to conserve storage space. As an example, convolution can allow data, to be packed or hashed in a manner that uses less space that the original data. Moreover, the convolved data can typically be de-convolved to the original data. One such use of data storage is in bulk data storage. Examples of bulk data storage can include networked storage, e.g., cloud storage, for example Elastic Cloud Storage offered by Dell EMC. Bulk storage can, in an aspect, manage disk capacity via partitioning of disk space in to blocks of fixed size, frequently referred to as chunks, for example a 128 MB chunk, etc. Chunks can be used to store user data, and the chunks can be shared among the same or different users, for example, one chunk may contain fragments of several user objects. A chunk's content can be modified in an append-only mode to prevent overwriting of data already added to the chunk. As such, when a typical chunk becomes full enough, it can be sealed so that the data therein is generally not able to be further modified. Accordingly, as user objects are updated, the updated user objects can be appended into another chunk. Eventually the earlier chunk can comprise only data that is stale. Storing stale data can be undesirable and the chunk can be reclaimed by a disk space management technology.
Some networked storage technologies are geographically distributed to enable storing chunks at different geographical locations. This can be beneficial in that data compromised at one location can be backed up by redundant data in another location. Chunks can be convolved to reduce the amount of disk space used by copies of data at geographically distinct locations. Convolution and de-convolution of chunks in different geographical locations can entail transferring data across a network, which transfer can be associated with time costs, monetary costs, etc.